Introduction to IBM Data Virtualization Manager for z/OS

Data Virtualization Manager for z/OS was released at the end of 2017. Since then, the market for data virtualization technology has increased significantly. Several trends contribute to the recognition that data virtualization is a must-have component for many enterprise organizations.

The growth in corporate transactional data and sources, Hadoop, IoT data, the acceptance of cloud as an enterprise datastore, and the availability and utility of public cloud services providing unique data are just some of the reasons contributing to the continuing exponential expansion of data volume and type. With data being generated and stored everywhere it has become impossible to centralize it all and maintain its accuracy. It is imperative, therefore, that data must be accessed where it is stored or originates.

Within your enterprise infrastructures, you have likely developed several to many large data repositories across several different vendor technologies. Each data repository will have its own unique capabilities and value. Each data repository vendor would likely tell you the same thing -you should move (i.e. copy) all your data to their platform. However, from a cost, efficiency, regulatory, security, and practical perspective this just does not make sense. Every copy of data has its own cost, latency, and risk. Copying all your data to wherever it might be needed would be expensive, un-timely, and presents potential regulatory, governance, and security risk issues.

Several years ago, the term “Data Gravity” was offered as an analogy to the concept of gravity in physics. Data gravity implies that data has mass, and therefore attracts other objects as physical objects do. The more data there is, the greater the mass, the more likely it is to attract other objects such as applications, services, and other data. Although the cloud often consumes a great deal of mindshare, most essential corporate data is still generated behind the firewall. And that data is typically transactional data, corporate treasure, so data gravity applies to the typical organization’s enterprise transactional servers. This is where the mainframe enters this expansive data story.

The mainframe is core to any organization’s enterprise infrastructure. Although we think that the mainframe services only the largest organizations, many smaller organizations benefit from its security, resiliency, low per-transaction cost, virtualization, high performance, and unique value. For many of these organizations, the mainframe is their primary transactional platform. According to IBM studies, 70 to 80 percent of all data originates behind the firewall and much of that originates on IBM Z. For many organizations their transactional systems have stayed largely unaltered for decades. Upgrades focused on the latest versions of software to stay up to date in line with support requirements, fine-tuning of applications, and small changes to applications to keep up with the times. Government agencies, banks, insurance companies, and financial services organizations have stayed with what served them well. Why change if something works? Today, organizations are under pressure to better serve their customers and bring new value to their constituents.

Although mobile has been around for some time, it is finally living up to its promise through the combination of analytics and cloud. Analytics are allowing organizations to drive greater value from their customer’s mobile use. Cloud is allowing organizations virtually limitless capacity to process data and take advantage of digital, momentary opportunities and provide those opportunities through their customer’s mobile devices. Mobile devices have largely become the de-facto interface with which customers interact with providers. Interestingly, significant percentageofIBM Z workloads are driven by mobile applications such as online banking and online purchases.

Mobile, analytics, and cloud are combining to completely rewrite industries and the rules of customer engagement. Digital transformation is offering unprecedented opportunities for organizations to improve customer relationships, acquire new customers and grow wallet share. Organizations are innovating with new ways to take advantage of their enterprise data assets and engage with customers.

Why is today different?

Many organizations’ IT architectures and the transaction systems, insight, and decision-making processes they support were designed decades ago. Traditional architectural approaches that depend solely on data movement can impede the ability to rapidly adapt to changing business cycles and adopt new development methods.

To deliver modern applications, organizations must:
 

Data, its’ use, and the insights it provides have a shelf-life. Some decisions require the most current data and real-time insight (at the point of interaction or transaction) improving decision making in areas like fraud detection and upsell/cross-sell efforts as well as supporting real-time opportunities, such as digital moments.

New architectural patterns need to consider a multi-speed approach to data delivery and consumption of data as well as a model that supports structured and semi-structured data across multiple disparate platforms. As your organization modernizes and takes advantage of new application development techniques, you should consider minimizing data movement as part of the modernization process. This will allow you to make the best use of your data as it exists in your operational systems.

Completely separating the systems of record from modern systems of engagement can result in customer defections, increased risk, and lost opportunity for improved operational productivity. We’ve all be frustrated by customer-facing applications that we know don’t reflect an organization’s current inventory or the status of a reservation. Digital opportunities are momentary. They must be based on the exact real inventory status to make offers to customers. This is quite different than traditional approaches using extensive data movement. Doing nothing –merely maintaining these existing approaches -opens the door for the competition to disrupt your business and attract your customers. New business with increased data volumes leads to higher resource usage and exacerbates an already challenging issue. Throwing hardware at the problem just pushes the problem off to another day. A change in approach is needed.

Hybrid cloud applications incorporate on-premises and on-cloud services and need to leverage all data across the enterprise. Modern applications are interconnected, interact through APIs, and enable customers and enterprises to digitally execute processes quickly. These applications need agile read/write access to IBM Z data, both relational and nonrelational, in an online environment. Applications that use traditional, scheduled batch programs to update transactional data can be refactored into modern applications that can access and update IBM Z data using modern APIs supported by Data Virtualization Manager for z/OS. And because Data Virtualization Manager for z/OS runs almost exclusively on readily available IBM Z Integrated Information Processors (zIIP), it doesn’t consume general processor capacity, and can potentially significantly reduce mainframe cost associated with ETL.

Data Virtualization Manager can virtualize legacy data sources, such as virtual storage access method (VSAM), adaptable database system (ADABAS), IBM IMSTM Database Manager, IBM Db2® for z/OS, and IBM System Management Facility (SMF). Its ability to federate these sources with virtually any other data brings the power of IBM Z to essentially any application, mobile, analytic, or cloud. And it does so with minimal additional processing costs and without the need for IBM Z skills or additional coding. Reducing complexity for accessing existing data applications implies less time to implement new engagement applications while also accessing real-time data. This means fewer unique skill sets are required to access complex legacy data structures. Complex application programming can now be done with simple SQL and NoSQL access. Updating or building modern applications using IBM Z data can produce elastic, interconnected, and more secure applications to deliver a competitive advantage.

Data Virtualization Manager for z/OS is optimized for the hardware it runs on and takes advantage of the new instruction sets available with each new IBM Z hardware platform. As IBM Z offers new hardware capabilities Data Virtualization Manager for z/OS inherits those advantages.

Any organization with data on IBM Z, whatever the data type, can benefit from Data Virtualization Manager for z/OS. Combine and integrate non-relational and relational data, integrate IBM Z and non-IBM Z data, and incorporate unstructured data with structured data. Provide SQL access to non-relational data. Modernize mainframe applications and enhance non-mainframe applications with mainframe data. Your alternatives are only limited to your imagination.

When your valuable data originates on IBM Z and moving it off platform exposes you to cost, latency, or risk, leveraging the processing on IBM Z can be your best alternative. IBM Data Virtualization Manager for z/OS -- access your data where it originates.